Jiali He

In machine learning, when part of the data with labels needs to be pro- cessed, it is termed as a semi-supervised learning algorithm. Dataset with missing attribute values or labels is referred to as incomplete information sys- tem. Addressing incomplete information within a system poses a significant challenge, which can be effectively tackled through the application of rough set theory (R-theory). However, R-theory has its limits, it fails to consider the frequency of an attribute value and then can not well fit the distribu- tion of attribute values. If we consider partially labeled data and replace a missing attribute value with the multiset of all possible attribute values under the same attribute, then it leads to the emergence of partially labeled multiset-valued data. In semi-supervised learning algorithm, in order to save time and cost, a large number of redundant features need to be deleted. This paper studies semi-supervised attribute selection (ss-attribute selec- tion) for partially labeled multiset-valued data. Initially, a partially labeled multiset-valued decision information system (p-MSVDIS) is partitioned into two distinct systems: a labeled multiset-valued decision information system (l-MSVDIS) and an unlabeled multiset-valued decision information system (u-MSVDIS). Subsequently, using the indistinguishable relation, distinguish- able relation, and dependence function, two types of attribute subset impor- tance in a p-MSVDIS are defined. They are the weighted sum of l-MSVDIS and u-MSVDIS determined by the missing rate of labels and can be regarded as a uncertainty measurement (UM) of a p-MSVDIS. Next, an adaptive ss- attribute selection algorithm for a p-MSVDIS is introduced, leveraging the degrees of importance, allowing for automatic adaptation to diverse missing rates. Finally, 10 datasets are used for experiment and statistical analysis, the outcomes show the proposed algorithm has their advantage than some algorithms.

This study investigated the validity and reliability of the Qualities of Online Programming Teacher Inventory (Q-OPTI). The Q-OPTI aimed to assess the qualities of online programming teachers. It consisted of six constructs with 38 indicators. The validity of the constructs and their items were determined through factor and discriminant validity analyses. Cronbach’s α analysis and composite reliability tests were used to determine the reliability of the construct items. The results of factor analysis revealed that six items were below the 0.50 threshold value, thereby decreasing the number of items to 32. Based on discriminant validity analysis, four of the six constructs were found to be valid. The item validity scores of the two items were not that far from the threshold value of 0.50. Given the theoretical and practical values of the instrument, all items and constructs were considered. Cronbach’s α and composite reliability tests revealed that all items were reliable. The instrument can explain 73 percent of the characteristics of an effective online programming instructor. As a result, it was concluded that the Q-OPTI is valid and reliable for determining and assessing the characteristics of an effective online programming teacher. Recommendations, limitations, and future work are also discussed.

Due to the high cost of labelling data, a lot of partially hybrid data are existed in many practical applications. Uncertainty measure (UM) can supply new viewpoints for analyzing data. They can help us in disclosing the substantive characteristics of data. Although there are some UMs to evaluate the uncertainty of hybrid data, they cannot be trivially transplanted into partially hybrid data. The existing studies often replace missing labels with pseudo-labels, but pseudo-labels are not real labels. When encountering high label error rates, work will be difficult to sustain. In view of the above situation, this paper studies four UMs for partially hybrid data and proposed semi-supervised attribute reduction algorithms. A decision information system with partially labeled hybrid data (p-HIS) is first divided into two decision information systems: one is the decision information system with labeled hybrid data (l-HIS) and the other is the decision information system with unlabeled hybrid data (u-HIS). Then, four degrees of importance on a attribute subset in a p-HIS are defined based on indistinguishable relation, distinguishable relation, dependence function, information entropy and information amount. We discuss the difference and contact among these UMs. They are the weighted sum of l-HIS and u-HIS determined by the missing rate and can be considered as UMs of a p-HIS. Next, numerical experiments and statistical tests on 12 datasets verify the effectiveness of these UMs. Moreover, an adaptive semi-supervised attribute reduction algorithm of a p-HIS is proposed based on the selected important degrees, which can automatically adapt to various missing rates. Finally, the results of experiments and statistical tests on 12 datasets show the proposed algorithm is statistically better than some stat-of-the-art algorithms according to classification accuracy.

Three-way decision (3WD) provides a new perspective for solving practical decision-making problems, which is in line with human’s cognitive pattern. A covering information system (CIS) is an information system (IS) that consists of multiple coverings in the universe. A CIS with decision attributes which is seen as a covering decision information system (CDIS). This paper proposes three-way group decisions in a CDIS, as well as gives its application on the problem of position competition. First of all, the neighbourhood of every point in a CDIS is defined, and corresponding similarity class of this point is also obtained. Then, because of the uncertainty of risks, loss functions are acquired through group decision-making by means of interval numbers. Next, a method of three-way group decisions in a CDIS is presented. Eventually, the position competition is presented as an example to support our proposed decision-making method.